OpenRaiser/Intern-Atlas huggingface dataset:
Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection
Venue: arXiv cs.CR 2025
Year: 2025

Network Intrusion Detection Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. This study explores the application of machine learning (ML) methods to improve the NIDS accuracy through analyzing intricate structures in deep-featured network traffic records. Leveraging the 1999 KDD CUP intrusion dataset as a benchmark, this research evaluates and optimizes several ML algorithms, including Support Vector Machines (SVM), Naïve Bayes variants (MNB, BNB), Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Trees (DT), AdaBoost, XGBoost, Logistic Regression (LR), Ridge Classifier, Passive-Aggressive (PA) Classifier, Rocchio Classifier, Artificial Neural Networks (ANN), and Perceptron (PPN). Initial evaluations without hyper-parameter optimization demonstrated suboptimal performance, highlighting the importance of tuning to enhance classification accuracy. After hyper-parameter optimization using grid and random search techniques, the SVM classifier achieved 99.12% accuracy with a 0.0091 False Alarm Rate (FAR), outperforming its default configuration (98.08% accuracy, 0.0123 FAR) and all other classifiers. This result confirms that SVM accomplishes the highest accuracy among the evaluated classifiers. We validated the effectiveness of all classifiers using a tenfold cross-validation approach, incorporating Recursive Feature Elimination (RFE) for feature selection to enhance the classifiers accuracy and efficiency. Our outcomes indicate that ML classifiers are both adaptable and reliable, contributing to enhanced accuracy in systems for detecting network intrusions.
Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms
Venue: arXiv cs.CY 2025
Year: 2025

Human behaviors are often guided or constrained by social norms, which are defined as shared, commonsense rules. For example, underlying an action ``\textit{report a witnessed crime}" are social norms that inform our conduct, such as ``\textit{It is expected to be brave to report crimes}''. Current AI systems that assess valence (i.e., support or oppose) of human actions by leveraging large-scale data training not grounded on explicit norms may be difficult to explain, and thus untrustworthy. Emulating human assessors by considering social norms can help AI models better understand and predict valence. While multiple norms come into play, conflicting norms can create tension and directly influence human behavior. For example, when deciding whether to ``\textit{report a witnessed crime}'', one may balance \textit{bravery} against \textit{self-protection}. In this paper, we introduce \textit{ClarityEthic}, a novel ethical assessment approach, to enhance valence prediction and explanation by generating conflicting social norms behind human actions, which strengthens the moral reasoning capabilities of language models by using a contrastive learning strategy. Extensive experiments demonstrate that our method outperforms strong baseline approaches, and human evaluations confirm that the generated social norms provide plausible explanations for the assessment of human behaviors.
Data Protection and Corporate Reputation Management in the Digital Era
Venue: arXiv cs.CR 2025
Year: 2025

This paper analyzes the relationship between cybersecurity management, data protection, and corporate reputation in the context of digital transformation. The study examines how organizations implement strategies and tools to mitigate cyber risks, comply with regulatory requirements, and maintain stakeholder trust. A quantitative research design was applied using an online diagnostic survey conducted among enterprises from various industries operating in Poland. The analysis covered formal cybersecurity strategies, technical and procedural safeguards, employee awareness, incident response practices, and the adoption of international standards such as ISO/IEC 27001 and ISO/IEC 27032. The findings indicate that most organizations have formalized cybersecurity frameworks, conduct regular audits, and invest in employee awareness programs. Despite this high level of preparedness, 75 percent of surveyed firms experienced cybersecurity incidents within the previous twelve months. The most frequently reported consequences were reputational damage and loss of customer trust, followed by operational disruptions and financial or regulatory impacts. The results show that cybersecurity is increasingly perceived as a strategic investment supporting long-term organizational stability rather than merely a compliance cost. The study highlights the importance of integrating cybersecurity governance with corporate communication and reputation management, emphasizing data protection as a key determinant of digital trust and organizational resilience.
SnapClass: An AI-Enhanced Classroom Management System for Block-Based Programming
Venue: arXiv cs.CY 2025
Year: 2025

Block-Based Programming (BBP) platforms, such as Snap!, have become increasingly prominent in K-12 computer science education due to their ability to simplify programming concepts and foster computational thinking from an early age. While these platforms engage students through visual and gamified interfaces, teachers often face challenges in using them effectively and finding all the necessary features for classroom management. To address these challenges, we introduce SnapClass, a classroom management system integrated within the Snap! programming environment. SnapClass was iteratively developed drawing on established research about the pedagogical and logistical challenges teachers encounter in computing classrooms. Specifically, SnapClass allows educators to create and customize block-based coding assignments based on student skill levels, implement rubric-based auto-grading, and access student code history and recovery features. It also supports monitoring student engagement and idle time, and includes a help dashboard with a raise hand feature to assist students in real time. This paper describes the design and key features of SnapClass those are developed and those are under progress.
Large Video Planner Enables Generalizable Robot Control
Venue: arXiv cs.RO 2025
Year: 2025

General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundation models by extending multimodal large language models (MLLMs) with action outputs, creating vision-language-action (VLA) systems. These efforts are motivated by the intuition that MLLMs' large-scale language and image pretraining can be effectively transferred to the action output modality. In this work, we explore an alternative paradigm of using large-scale video pretraining as a primary modality for building robot foundation models. Unlike static images and language, videos capture spatio-temporal sequences of states and actions in the physical world that are naturally aligned with robotic behavior. We curate an internet-scale video dataset of human activities and task demonstrations, and train, for the first time at a foundation-model scale, an open video model for generative robotics planning. The model produces zero-shot video plans for novel scenes and tasks, which we post-process to extract executable robot actions. We evaluate task-level generalization through third-party selected tasks in the wild and real-robot experiments, demonstrating successful physical execution. Together, these results show robust instruction following, strong generalization, and real-world feasibility. We release both the model and dataset to support open, reproducible video-based robot learning. Our website is available at https://www.boyuan.space/large-video-planner/.
VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces
Venue: arXiv cs.CR 2025
Year: 2025

Recent advances in large language models (LLMs) have enabled a new generation of autonomous agents that operate over sustained periods and manage sensitive resources on behalf of users. Trusted for their ability to act without direct oversight, such agents are increasingly considered in high-stakes domains including financial management, dispute resolution, and governance. Yet in practice, agents execute on infrastructure controlled by a host, who can tamper with models, inputs, or outputs, undermining any meaningful notion of autonomy. We address this gap by introducing VET (Verifiable Execution Traces), a formal framework that achieves host-independent authentication of agent outputs and takes a step toward host-independent autonomy. Central to VET is the Agent Identity Document (AID), which specifies an agent's configuration together with the proof systems required for verification. VET is compositional: it supports multiple proof mechanisms, including trusted hardware, succinct cryptographic proofs, and notarized TLS transcripts (Web Proofs). We implement VET for an API-based LLM agent and evaluate our instantiation on realistic workloads. We find that for today's black-box, secret-bearing API calls, Web Proofs appear to be the most practical choice, with overhead typically under 3$\times$ compared to direct API calls, while for public API calls, a lower-overhead TEE Proxy is often sufficient. As a case study, we deploy a verifiable trading agent that produces proofs for each decision and composes Web Proofs with a TEE Proxy. Our results demonstrate that practical, host-agnostic authentication is already possible with current technology, laying the foundation for future systems that achieve full host-independent autonomy.
BRAID: Bounded Reasoning for Autonomous Inference and Decisions
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) exhibit nonlinear relationships between performance, cost, and token usage. This paper presents a quantitative study on structured prompting using BRAID (Bounded Reasoning for Au tonomous Inference and Decisions) across multiple GPT model tiers, eval uated on the AdvancedIF, GSM-Hard, and the SCALE MultiChallenge benchmark datasets. BRAID introduces a bounded reasoning framework using Mermaid-based instruction graphs that enable models to reason struc turally rather than through unbounded natural-language token expansion. We show that structured machine-readable prompts substantially increase reasoning accuracy and cost efficiency for agents in production systems. The findings establish BRAID as an effective and scalable technique for optimizing inference efficiency in autonomous agent systems. All datasets and detailed result logs are available at https://benchmark.openserv.ai.
Security Aspects of ISO 15118 Plug and Charge Payment
Venue: arXiv cs.CR 2025
Year: 2025

For the rise of electric vehicles, especially for long-distance driving, minimizing charging times is vital. While multiple standards for DC fast charging exist, the leading standard in Europe is ISO 15118. In theory, this standard is accompanied by a variety of security controls, ensuring the authenticity and confidentiality of charging communication, as well as the exchange of payment information. In practice, these security controls are insufficient for effectively securing charging communication. In this paper, we go through all security controls defined in ISO 15118 and demonstrate their shortcomings. Most notably, we present a previously unpublished vulnerability in the plug and charge functionality of ISO 15118. We provide a proof-of-concept implementation of this vulnerability, which, allows a vehicle to be charged while a second, victim vehicle is billed for it. Additionally, we define an alternative plug and charge authentication scheme, which requires fewer efforts towards certificate enrollment and promises to be more resilient and future-proof. Our findings should be considered when implementing and advancing the standard, as the mitigation of the discovered vulnerability is critical for the security of fast charging.
Provably Extracting the Features from a General Superposition
Venue: arXiv cs.LG 2025
Year: 2025

It is widely believed that complex machine learning models generally encode features through linear representations, but these features exist in superposition, making them challenging to recover. We study the following fundamental setting for learning features in superposition from black-box query access: we are given query access to a function \[ f(x)=\sum_{i=1}^n a_i\,σ_i(v_i^\top x), \] where each unit vector $v_i$ encodes a feature direction and $σ_i:\mathbb{R} \rightarrow \mathbb{R}$ is an arbitrary response function and our goal is to recover the $v_i$ and the function $f$. In learning-theoretic terms, superposition refers to the overcomplete regime, when the number of features is larger than the underlying dimension (i.e. $n > d$), which has proven especially challenging for typical algorithmic approaches. Our main result is an efficient query algorithm that, from noisy oracle access to $f$, identifies all feature directions whose responses are non-degenerate and reconstructs the function $f$. Crucially, our algorithm works in a significantly more general setting than all related prior results -- we allow for essentially arbitrary superpositions, only requiring that $v_i, v_j$ are not nearly identical for $i \neq j$, and general response functions $σ_i$. At a high level, our algorithm introduces an approach for searching in Fourier space by iteratively refining the search space to locate the hidden directions $v_i$.
Maintaining the Level of a Payload carried by Multi-Robot System on Irregular Surface
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we introduce a multi robot payload transport system to carry payloads through an environment of unknown and uneven inclinations while maintaining the desired orientation of the payload. For this task, we used custom built robots with a linear actuator (pistons) mounted on top of each robot. The system continuously monitors the payload's orientation and computes the required piston height of each robot to maintain the desired orientation of the payload. In this work, we propose an open loop controller coupled with a closed loop PID controller to achieve the goal. As our modelling makes no assumptions on the type of terrain, the system can work on any unknown and uneven terrains and inclinations. We showcase the efficacy of our proposed controller by testing it on various simulated environments with varied and complex terrains.
SWIFT-Nav: Stability-Aware Waypoint-Level TD3 with Fuzzy Arbitration for UAV Navigation in Cluttered Environments
Venue: arXiv cs.RO 2025
Year: 2025

Efficient and reliable UAV navigation in cluttered and dynamic environments remains challenging. We propose SWIFT-Nav: Stability-aware Waypoint-level Integration of Fuzzy arbitration and TD3 for Navigation, a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths. The system couples a sensor-driven perception front end with a TD3 waypoint policy: the perception module converts LiDAR ranges into a confidence-weighted safety map and goal cues, while the TD3 policy is trained with Prioritised Experience Replay to focus on high-error transitions and a decaying epsilon-greedy exploration schedule that gradually shifts from exploration to exploitation. A lightweight fuzzy-logic layer computes a safety score from radial measurements and near obstacles, gates mode switching and clamps unsafe actions; in parallel, task-aligned reward shaping combining goal progress, clearance, and switch-economy terms provides dense, well-scaled feedback that accelerates learning. Implemented in Webots with proximity-based collision checking, our approach consistently outperforms baselines in trajectory smoothness and generalization to unseen layouts, while preserving real-time responsiveness. These results show that combining TD3 with replay prioritisation, calibrated exploration, and fuzzy-safety rules yields a robust and deployable solution for UAV navigation in cluttered scenes.
Cross-Language Bias Examination in Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a prompt-based Implicit Association Test. By translating the prompts and word list into five target languages, English, Chinese, Arabic, French, and Spanish, we directly compare different types of bias across languages. The results reveal substantial gaps in bias across languages used in LLMs. For example, Arabic and Spanish consistently show higher levels of stereotype bias, while Chinese and English exhibit lower levels of bias. We also identify contrasting patterns across bias types. Age shows the lowest explicit bias but the highest implicit bias, emphasizing the importance of detecting implicit biases that are undetectable with standard benchmarks. These findings indicate that LLMs vary significantly across languages and bias dimensions. This study fills a key research gap by providing a comprehensive methodology for cross-lingual bias analysis. Ultimately, our work establishes a foundation for the development of equitable multilingual LLMs, ensuring fairness and effectiveness across diverse languages and cultures.
Topic Discovery and Classification for Responsible Generative AI Adaptation in Higher Education
Venue: arXiv cs.AI 2025
Year: 2025

As generative artificial intelligence (GenAI) becomes increasingly capable of delivering personalized learning experiences and real-time feedback, a growing number of students are incorporating these tools into their academic workflows. They use GenAI to clarify concepts, solve complex problems, and, in some cases, complete assignments by copying and pasting model-generated contents. While GenAI has the potential to enhance learning experience, it also raises concerns around misinformation, hallucinated outputs, and its potential to undermine critical thinking and problem-solving skills. In response, many universities, colleges, departments, and instructors have begun to develop and adopt policies to guide responsible integration of GenAI into learning environments. However, these policies vary widely across institutions and contexts, and their evolving nature often leaves students uncertain about expectations and best practices. To address this challenge, the authors designed and implemented an automated system for discovering and categorizing AI-related policies found in course syllabi and institutional policy websites. The system combines unsupervised topic modeling techniques to identify key policy themes with large language models (LLMs) to classify the level of GenAI allowance and other requirements in policy texts. The developed application achieved a coherence score of 0.73 for topic discovery. In addition, GPT-4.0-based classification of policy categories achieved precision between 0.92 and 0.97, and recall between 0.85 and 0.97 across eight identified topics. By providing structured and interpretable policy information, this tool promotes the safe, equitable, and pedagogically aligned use of GenAI technologies in education. Furthermore, the system can be integrated into educational technology platforms to help students understand and comply with relevant guidelines.
CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Streamflow forecasting is crucial for water resource management and risk mitigation. While deep learning models have achieved strong predictive performance, they often overlook underlying physical processes, limiting interpretability and generalization. Recent causal learning approaches address these issues by integrating domain knowledge, yet they typically rely on fixed causal graphs that fail to adapt to data. We propose CauStream, a unified framework for causal spatiotemporal streamflow forecasting. CauSTream jointly learns (i) a runoff causal graph among meteorological forcings and (ii) a routing graph capturing dynamic dependencies across stations. We further establish identifiability conditions for these causal structures under a nonparametric setting. We evaluate CauSTream on three major U.S. river basins across three forecasting horizons. The model consistently outperforms prior state-of-the-art methods, with performance gaps widening at longer forecast windows, indicating stronger generalization to unseen conditions. Beyond forecasting, CauSTream also learns causal graphs that capture relationships among hydrological factors and stations. The inferred structures align closely with established domain knowledge, offering interpretable insights into watershed dynamics. CauSTream offers a principled foundation for causal spatiotemporal modeling, with the potential to extend to a wide range of scientific and environmental applications.
MultiPath Transfer Engine: Breaking GPU and Host-Memory Bandwidth Bottlenecks in LLM Services
Venue: arXiv cs.DC 2025
Year: 2025

The limited bandwidth of PCIe has emerged as the critical bottleneck for large language model (LLM) performance, such as prefix cache fetching and model switching. Although intra-server multipath data transfer between GPU and host memory is theoretically possible, heterogeneous protocols such as PCIe and NVLink currently limit the bandwidth between host memory and GPUs to that of a single PICe link. This limitation resuals in underutilized intra-server bandwidth. To address this issue, we propose Multipath Memory Access (MMA), a scheme that, to the best of our knowledge, is the first to enalbe efficient multipath data transfer between GPU and host memory. MMA supports seamless deployment via dynamic library injection, enabling LLM applications to benefit from MMA without requiring any code modification. In our testbed, MMA significantly improves the data transfer bandwidth between the GPU and memory, achieving a peak bandwidth of 245 GB/s-representing a 4.62x speedup compared to the natice single-path bandwidth. End-to-end evaluations demonstrate that MMA reduces the time-to-first-token (TTFT) for LLM serving by 1.14x to 2.38x and decreases model-switching latency in vLLM's sleep mode by 1.12x to 2.48x.
Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil
Venue: arXiv cs.ET 2025
Year: 2025

Widespread Pb (lead) contamination of urban soil significantly impacts food safety and public health and hinders city greening efforts. However, most existing technologies for measuring Pb are labor-intensive and costly. In this study, we propose SoilScanner, a radio frequency-based wireless system that can detect Pb in soils. This is based on our discovery that the propagation of different frequency band radio signals is affected differently by different salts such as NaCl and Pb(NO3)2 in the soil. In a controlled experiment, manually adding NaCl and Pb(NO3)2 in clean soil, we demonstrated that different salts reflected signals at different frequencies in distinct patterns. In addition, we confirmed the finding using uncontrolled field samples with a machine learning model. Our experiment results show that SoilScanner can classify soil samples into low-Pb and high-Pb categories (threshold at 200 ppm) with an accuracy of 72%, with no sample with > 500 ppm of Pb being misclassified. The results of this study show that it is feasible to build portable and affordable Pb detection and screening devices based on wireless technology.